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On-line partial discharge analysis of transmission and distribution assets

On-line partial discharge analysis of transmission and distribution assets
On-line partial discharge analysis of transmission and distribution assets
It is becoming increasingly clear that methodologies for PD classification based on standard laboratory experimental data are not readily applicable when assessing online PD data measured in the field. It is not just that field data is corrupted by noise and disturbance, but also the significant differences between typical laboratory experiments to generate PD data and the generation of PDs in high voltage plant due to degradation of the insulation system. In this paper, the use of nonlinear time-series analysis on field data is shown to yield useful information, methods involving dimension reduction techniques are shown to allow identification of different sources and finally a method for designing standard finite impulse response filters that approximate the nonlinear analytical approach and are easy to implement in condition monitoring systems are discussed.
partial discharge, condition monitoring, clustering, time series analysis, stochastic analysis
978-1-4799-2789-0
180-184
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e

Lewin, P.L. (2014) On-line partial discharge analysis of transmission and distribution assets. 2014 IEEE Electrical Insulation Conference (EIC), United States. 08 - 11 Jun 2014. pp. 180-184 . (doi:10.1109/EIC.2014.6869371).

Record type: Conference or Workshop Item (Paper)

Abstract

It is becoming increasingly clear that methodologies for PD classification based on standard laboratory experimental data are not readily applicable when assessing online PD data measured in the field. It is not just that field data is corrupted by noise and disturbance, but also the significant differences between typical laboratory experiments to generate PD data and the generation of PDs in high voltage plant due to degradation of the insulation system. In this paper, the use of nonlinear time-series analysis on field data is shown to yield useful information, methods involving dimension reduction techniques are shown to allow identification of different sources and finally a method for designing standard finite impulse response filters that approximate the nonlinear analytical approach and are easy to implement in condition monitoring systems are discussed.

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Published date: 8 June 2014
Venue - Dates: 2014 IEEE Electrical Insulation Conference (EIC), United States, 2014-06-08 - 2014-06-11
Keywords: partial discharge, condition monitoring, clustering, time series analysis, stochastic analysis
Organisations: EEE

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Local EPrints ID: 366645
URI: http://eprints.soton.ac.uk/id/eprint/366645
ISBN: 978-1-4799-2789-0
PURE UUID: 2431c48c-d26b-4190-9889-442f72ca4221

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Date deposited: 04 Jul 2014 11:18
Last modified: 16 Dec 2019 20:24

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Author: P.L. Lewin

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